Mode Collapse. 
Mode collapse is the main catastrophic problem during the adversarial training stage of GANs, which leads to the diversity of generated images are monotonous, only fitting part of the real data distribution.
To address this issue, recent efforts were focus on new objective functions [1]}, [2]}, [3]}, architecture modifications [4]}, [5]} and mini-batch discrimination [6]}, [7]}.
Actually, the capacity gap between the generator and the discriminator is a more essential reason for mode collapse.
In this paper, we provide a different perspective to eliminate the capacity gap, we select the convolutional channel to be activated in the discriminator according to a local capacity constraint and a global coordination constraint.
